85 its driver (cargo, destination, schedule, route, mechanical condition, driver’s history, etc.). They can integrate all this information and determine within milliseconds whether to alert a dispatcher to solve a problem. Together, they work at scale to intelligently track the intricate workings of a massive system like a trucking fleet. The applications are countless. Digital twins can analyse telemetry from IoT devices in cities to monitor traffic sensors, sounds at intersections, and gas leak detectors. They can analyse biometric data from health-tracking devices to look for medical issues, and they can watch entry points in a large factory to maintain security. These are just a few examples. A major challenge remains. How can digital twins incorporate real-time analytics algorithms that effectively pick out the signal from the noise in streams of telemetry? How can they be designed to alert when needed but not unnecessarily? For example, when does the telemetry from a truck engine indicate that maintenance will be needed? It turns out that machine learning algorithms are especially well-suited for tackling problems like these. They can easily be trained using historical data to look for anomalies in groups of telemetry, for example, engine parameters, like oil temperature, oil pressure, and revolutions per minute. Once they are trained to recognise what’s normal and what’s abnormal, they can then be turned loose to run within digital twins and process live data. Because machine learning algorithms don’t need to know why certain telemetry combinations are abnormal, they can be applied to a wide variety of applications. Incorporating machine learning into digital twins takes them well beyond their roots in PLM and opens the door for their rapid adoption in live systems where effective real-time analytics is essential. Whether it’s for our supply chains, security or healthcare, digital twins with real-time analytics and machine learning will undoubtedly play a key role. William Bain is CEO of ScaleOut Software
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